The 2023 ImageCLEFmedical GANs task is the first edition of this task, examining the existing hypothesis that GANs (Generative Adversarial Networks) are generating medical images that contain the “fingerprints” of the real images used for generative network training. The objective proposed to the participants is to identify the real images that were used to obtain some synthetic images using Generative Models. Overall, 23 teams registered to the task, 8 of them finalizing the task and submitting runs. A total of 40 runs were received. An analysis of the proposed methods shows a great diversity among them, ranging from texture analysis, similarity-based approaches that join inducer predictions like SVM or KNN, to deep learning approaches and even multi-stage transfer learning. This paper presents the overview of 2023 ImageCLEFmedical GANs task by describing its datasets, evaluation metrics as well as a discussion of the participants runs and results, and the future challenges.